Ai In Cyber Security- From Fundamentals To Hands-On Soc Auto

AI techniques for cyber defense — from machine learning and anomaly detection to SOC automation, adversarial AI

What you'll learn

Understand AI Applications in Security Operations

Analyze and Detect Threats Using AI Tools

Implement AI-Powered Security Automation

Build and Evaluate Machine Learning Models for Cybersecurity

Integrate AI into SOC Operations & Compliance

Requirements

Basic Computer Skills – Comfort with using a computer, installing software, and navigating files.

Fundamental Cybersecurity Awareness (Optional but Helpful) – Understanding of basic concepts like networks, threats, and firewalls is useful, but not mandatory.

Familiarity with Python (Optional) – Some labs use Python for data analysis and machine learning. Step-by-step guidance will be provided for beginners.

Tools & Equipment – A laptop/desktop with internet access (Windows/Mac/Linux) and the ability to install free/open-source tools (e.g., Python, Jupyter, security log datasets).

Description

Unlock the power of Artificial Intelligence in Cyber Security.This course takes you from the foundations of AI and machine learning to building hands-on threat detection models, applying AI to real-world SOC operations, and preparing for the future of AI-driven defense.With step-by-step labs, real datasets, case studies, and practical workflows, you’ll learn not just theory but how to implement AI in your own security environment.What You’ll LearnUnderstand the core AI & ML concepts used in cyber defenseApply machine learning for intrusion detection and anomaly detectionBuild and evaluate deep learning models for zero-day attack detectionUse AI for log analytics, CTI, and SOC workflowsExplore adversarial AI risks and defensesDevelop a full end-to-end threat detection pipelineIntegrate AI with SOC tools like Splunk, Sentinel, and n8nAnalyze industry case studies (Google, Microsoft, startups)Anticipate the future of AI in security: SOC automation, federated learning, quantum security, and ethical challengesHands-On Labs IncludeBuilding intrusion detection with ML modelsDeep learning for anomaly detection (autoencoders)NLP for phishing email detectionMalware classification using ML featuresFraud detection with anomaly detection modelsEnd-to-end threat detection pipeline with deployment simulationSOC automation preview with n8n playbooksWho This Course Is ForCybersecurity professionals who want to add AI/ML skills to their toolkitSOC analysts & engineers looking to automate detection & responseData scientists & ML engineers exploring applications in cybersecurityStudents & career changers interested in AI-driven cyber defense

Overview

Section 1: Foundations

Lecture 1 Introduction to AI in Cyber Security

Lecture 2 Overview of Artificial Intelligence

Lecture 3 Importance of AI in Cyber Security

Lecture 4 Applications of AI in Cyber Security

Section 2: Core AI Techniques for Security

Lecture 5 Machine Learning for Threat Detection

Lecture 6 Understanding Machine Learning

Lecture 7 Data-driven models vs rule-based systems

Lecture 8 Training, Testing and Evaluation

Lecture 9 Supervised and Unsupervised Learning

Lecture 10 Machine Learning Models for Threat Detection

Lecture 11 Lab 2.1 — Machine Learning for Threat Detection

Lecture 12 Deep Learning for Anomaly Detection

Lecture 13 Basics of Deep Learning

Lecture 14 Neural Networks and Deep Learning Architectures

Lecture 15 Active Functions

Lecture 16 Autoencoders for Anomaly Detection

Lecture 17 Case Study – Detecting Abnormal Logins

Lecture 18 Benefits & Challenges

Lecture 19 Summary

Lecture 20 Lab_2_2_DeepLearning_AnomalyDetection

Section 3: AI in Security Operations

Lecture 21 AI-Powered Security Analytics

Lecture 22 Lab_3_1_AI_Powered_Security_Analytics

Lecture 23 Security Data Analytics

Lecture 24 Lab 3.2 — Security Data Analytics

Lecture 25 Role of AI in Security Analytics

Lecture 26 Lab 3.3 — Role of AI in Security Analytics (Anomaly Detection)

Lecture 27 Benefits and Challenges of AI-Powered Security Analytics

Lecture 28 Lab 3.4 — Benefits & Challenges of AI

Lecture 29 Cyber Threat Intelligence with AI

Lecture 30 Lab_3.5_Cyber_Threat_Intelligence_with_AI

Lecture 31 Concept of Cyber Threat Intelligence

Lecture 32 Lab 3.6 — Concept of Cyber Threat Intelligence (CTI)

Lecture 33 Enhancing Threat Intelligence with AI

Lecture 34 Lab 3.7 — Enhancing CTI with AI

Lecture 35 AI Tools for Cyber Threat Intelligence

Section 4: Advanced Topics & Risks

Lecture 36 Section Introduction

Lecture 37 Adversarial AI & AI Security Risks

Lecture 38 Adversarial Machine Learning

Lecture 39 Risks of Over-Reliance on AI

Lecture 40 Defensive Strategies Against Adversarial AI

Lecture 41 Lab 4.1 — Evasion Attacks with FGSM on MNIST

Lecture 42 Lab 4.2 — Poisoning Attack on Text Spam Classifier

Lecture 43 Lab 4.3 — Adversarial Training for Robustness

Section 5: Practical Applications

Lecture 44 Practical AI in Cyber Security (Hands-On & Case Studies)

Lecture 45 AI in Action - Real-world Cybersecurity Use Cases

Lecture 46 Tools & Frameworks

Lecture 47 Building a Simple Threat Detection Model - EndtoEnd -Threat Life Cycle(Hands-On)

Lecture 48 Case Studies from Industry

Section 6: The Road Ahead

Lecture 49 Future Trends in AI & Cyber Security

Lecture 50 Emerging Technologies in AI for Cyber Security

Lecture 51 Challenges and Opportunities in the Future

Lecture 52 Ethical Considerations and Privacy Issues

Lecture 53 Add-on: n8n Workflow for SOC Operations

Section 7: Course Summary

Lecture 54 Course Summary

Aspiring Cybersecurity Professionals – Students or beginners who want to break into the cybersecurity field with cutting-edge AI skills.,SOC Analysts & IT Security Teams – Professionals looking to enhance their threat detection, incident response, and log analysis capabilities with AI-driven tools.,Data Science & AI Enthusiasts – Learners curious about applying machine learning to real-world security problems.,IT Administrators & Network Engineers – Those who want to automate monitoring, anomaly detection, and compliance tasks.,Business & Technology Leaders – Managers and decision-makers who need to understand how AI can optimize security operations and reduce risks.